Photovoltaic support adaptive regulation and control system and method based on artificial intelligence, and readable storage medium

Abstract

The invention relates to a photovoltaic support adaptive regulation and control system and method based on artificial intelligence and a readable storage medium, and relates to the field of photovoltaic panel regulation and control. Comprising a control unit, an analysis unit, an environment detection unit, an evaluation unit and an execution unit. The environment detection unit is used for detecting surrounding environment data and feeding back the data to the analysis unit, the evaluation unit and the control unit; the analysis unit is used for receiving data of the environment detection unit and generating decision suggestions; and the control unit is used for receiving data of the environment detection unit, extracting environment characteristics and obtaining adjustment data according to decision suggestions of the analysis unit. According to the invention, predictive feedback is realized, the analysis and prediction of environmental data are carried out through the analysis unit and the control unit, and the system can make an accurate decision, thereby realizing predictive feedback, adjusting the angle of the photovoltaic panel in advance, adapting to future weather changes, and improving the stability and power generation efficiency of the system.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic panel control, and in particular to an artificial intelligence-based photovoltaic support adaptive control system, method and readable storage medium. Background Art

[0002] With the deterioration of the environment and the deepening of human environmental awareness, photovoltaic power generation, as one of the main green energy sources, is increasingly being used. Since the light angles in regions at different latitudes are different, the installation angle of photovoltaic panels needs to be adjusted. At the same time, the sun rises in the east and sets in the west every day, and the angle of traditional photovoltaic panels is fixed and difficult to change. In this case, the time period that the photovoltaic panels can face the sunlight is actually not long, so the power generation effect of the photovoltaic panels is not very efficient.

[0003] In the prior art, the automatic tracking of photovoltaic brackets is usually based on simple sensor data or preset motion trajectories, which often cannot fully adapt to complex and changing environmental conditions. Summary of the invention

[0004] In view of this, the present invention aims to propose an artificial intelligence-based photovoltaic support adaptive control system to solve the problems in the prior art.

[0005] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0006] The present invention proposes an artificial intelligence-based photovoltaic support adaptive control system:

[0007] It includes a control unit, an analysis unit, an environment detection unit, an evaluation unit and an execution unit;

[0008] The environment detection unit is used to detect the surrounding environment data and feed the data back to the analysis unit, the evaluation unit and the control unit;

[0009] The analysis unit is used to receive data from the environment detection unit and generate decision suggestions;

[0010] The control unit is used to receive data from the environment detection unit, extract environmental features and obtain adjustment data according to the decision suggestions of the analysis unit;

[0011] The execution unit is used to receive data from the control unit and adjust the position angle of the photovoltaic panel;

[0012] The evaluation unit can evaluate the overall performance based on the adjustment data of the control unit and the decision suggestions of the analysis unit.

[0013] Furthermore, the control unit includes a collection module, a control module, and a prediction module;

[0014] The collection module is used to receive information from the environment detection unit and extract data features according to a convolutional neural network feature extraction algorithm;

[0015] The prediction module is used to input data features into the feature calculation prediction model to obtain prediction results;

[0016] The control module is used to send adjustment data to the execution unit to adjust the angle of the photovoltaic panel.

[0017] Furthermore, the convolutional neural network feature extraction algorithm extraction formula is as follows:

[0018] conv(xw)=σ(x * w+bconv)

[0019] Where: conv is the convolution operation; σ is the ReLU function; * is the convolution operation; x is the input data; w and b are the weights and biases of the model, which are learned by training the network.

[0020] Furthermore, the feature calculation prediction model is calculated by the following formula:

[0021] y=β1x1+β2x2+…+βnxn+b

[0022] Where: y is the prediction result, which is the optimal tilt angle of the photovoltaic panel; x1, x2, ..., xn are the extracted eigenvalues; β1, β2, ..., βn and b are the weights and biases of the model, which are learned by training the model.

[0023] Further, the analysis unit includes a decision module and a storage module for storing historical data;

[0024] The analysis unit is used to obtain decision suggestions through the following formula:

[0025] D=F(H,C,P(C),S)

[0026] Where: D is the decision structure; H is the historical data environment data; C is the implementation environment data; P(C) is the prediction of the future environment; F(.) is the decision function. The relationship between H, C, P(C) and S is learned through a deep neural network, and the optimal D is predicted based on the current input.

[0027] Furthermore, the analysis unit also includes an optimization module, which obtains a final decision by combining a neural network and a support vector machine through voting or weighted averaging.

[0028] Furthermore, the evaluation unit obtains the evaluation data through the following formula:

[0029] Eout=η·G·A·cos(θ)·f(C,D,S,M)

[0030] Where: Eout represents the output power of the photovoltaic system; η is the energy conversion efficiency of the photovoltaic panel; G is the intensity of incident solar radiation; A is the effective area of ​​the photovoltaic panel; θ is the angle between the incident light and the normal of the photovoltaic panel;

[0031] f(.) is a comprehensive function; C is the environmental data collected by the environmental sensor; D represents the decision-making recommendations of the analysis unit; S represents the self-learning and optimization of the control unit; M represents the adjustment of the photovoltaic panel position by the actuator;

[0032] The actuator adjusts the position of the photovoltaic panel M according to y generated by the prediction model.

[0033] The present invention also proposes a photovoltaic support control method, which is applied to the above-mentioned photovoltaic support adaptive control system based on artificial intelligence:

[0034] The control method comprises the following steps:

[0035] S1, the environment detection unit collects real-time environment data and transmits it to the analysis unit, control unit and evaluation unit;

[0036] S2, the analysis unit generates decision suggestions based on historical data, real-time data, future predictions, and control unit status;

[0037] S3, the control unit receives the decision of the analysis unit and calculates the optimal angle of the photovoltaic panel based on its own learning optimization results;

[0038] S4, the evaluation unit evaluates the adjusted performance according to the information from the analysis unit and the control unit;

[0039] S5, the control unit sends the final adjustment information to the execution unit, and the execution unit controls the photovoltaic panel to adjust the angle.

[0040] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the program implements the steps of the above method when executed by a processor.

[0041] Compared with the prior art, the present invention has the following advantages:

[0042] In the present invention, adaptive control of the photovoltaic support is achieved: the system can calculate the optimal angle of the photovoltaic panel based on environmental data to achieve adaptive control and maximize the efficiency of solar energy utilization.

[0043] Continuous learning and self-optimization are achieved: the analysis unit can use the artificial intelligence semantic big model cloud to analyze the decisions given by the server, and perform self-optimization through continuous learning of environmental data, constantly improving decision-making and control strategies to adapt to changing environmental conditions and improve the adaptability and efficiency of the system.

[0044] Realized predictive feedback: By analyzing and predicting environmental data through the analysis unit and control unit, the system can make accurate decisions, thereby realizing predictive feedback, adjusting the angle of the photovoltaic panels in advance, adapting to future weather changes, and improving the stability of the system and power generation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0046] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0047] Figure 2 It is a structural block diagram of the control system of the present invention;

[0048] Figure 3 is a structural block diagram of a control unit of the present invention;

[0049] Figure 4 is a structural block diagram of an analysis unit of the present invention;

[0050] Figure 5 The figure is a flow chart of the control method of the present invention. DETAILED DESCRIPTION

[0051] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0052] In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper", "lower", "inner", "back", etc. are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0053] In addition, in the description of the present invention, unless otherwise clearly defined, the terms "installed", "connected", "connected", and "connector" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in combination with specific circumstances.

[0054] The following will refer to the attached Figures 1 to 5 The present invention is described in detail with reference to embodiments.

[0055] Embodiment 1

[0056] In general, this embodiment relates to an artificial intelligence-based photovoltaic support adaptive control system, such as Figure 1 and Figure 2 As shown, the control system includes a control unit, an analysis unit, an environment detection unit, an evaluation unit and an execution unit. The environment detection unit is used to detect the surrounding environmental data and feed the data back to the analysis unit, the evaluation unit and the control unit;

[0057] An analysis unit, used to receive data from the environment detection unit and generate decision suggestions;

[0058] A control unit, used to receive data from the environment detection unit, extract environmental features and obtain adjustment data according to the decision suggestions of the analysis unit;

[0059] An execution unit, used to receive data from the control unit and adjust the position angle of the photovoltaic panel;

[0060] The evaluation unit is capable of evaluating the overall performance based on the adjustment data of the control unit and the decision suggestions of the analysis unit.

[0061] In this embodiment, the control unit receives the sensor data, analyzes the given decision, and calculates the optimal angle of the photovoltaic panel. The actuator adjusts the position of the photovoltaic panel according to the instructions of the control unit to maximize the efficiency of solar energy utilization. The evaluation unit can calculate the adjusted performance to facilitate the subsequent optimization adjustment of the control unit and the analysis unit.

[0062] Among them, the environment detection unit is used to detect the surrounding environmental data and feed the data back to the analysis unit, evaluation unit and control unit. In specific implementation, the environment detection unit is an environmental sensor. The working principle of the environmental sensor is based on various physical, chemical or biological effects, such as thermoelectric effect, piezoelectric effect, photoelectric effect, chemical reaction, etc. These effects cause the electrical parameters of the sensor, such as resistance, capacitance, inductance, to change, thereby realizing the measurement of environmental quantities. The sensor converts the collected information into a signal that the device can process for subsequent analysis and processing.

[0063] The analysis unit's decision-making combines multiple factors, comprehensively considers historical and real-time data, and predictions of future environments to provide optimal decision recommendations (such as the optimal tilt angle of photovoltaic panels).

[0064] like Figure 4 As shown, the analysis unit includes a decision module and a storage module for storing historical data;

[0065] The analysis unit is used to obtain decision suggestions through the following formula:

[0066] D=F(H,C,P(C),S)

[0067] Where: D is the decision structure; H is the historical data environment data; C is the implementation environment data; P(C) is the prediction of the future environment; F(.) is the decision function. The relationship between H, C, P(C) and S is learned through a deep neural network, and the optimal D is predicted based on the current input.

[0068] The analysis unit also includes an optimization module, which combines a neural network and a support vector machine to obtain a final decision by voting or weighted averaging.

[0069] In detail, the analysis unit continuously learns industry best practices and a large amount of historical meteorological data, including temperature, humidity, wind speed, air pressure, precipitation and other meteorological variables. It extracts features from meteorological data, identifies and learns the complex relationships and patterns between meteorological variables, and achieves accurate predictions of future weather. It uses a deep neural network to learn the relationship between H, C, P(C), and S through the formula D=F(H,C,P(C),S), interacts with the environment through reinforcement learning and ensemble learning, and optimizes decision-making strategies based on reward signals. It combines neural networks and support vector machines to obtain final decisions through voting or weighted averaging.

[0070] It should be noted that the analysis unit is preferably an artificial intelligence semantic big model cloud analysis server. The artificial intelligence semantic big model cloud analysis server uses large-scale semantic models and deep learning algorithms to analyze and predict environmental data, and can achieve accurate predictions of future weather through continuous learning, and then make accurate decisions.

[0071] In addition, if Figure 3 As shown, the above control unit includes a collection module, a control module, and a prediction module;

[0072] The collection module is used to receive information from the environment detection unit and extract data features according to the convolutional neural network feature extraction algorithm;

[0073] The prediction module is used to input data features into the feature calculation prediction model to obtain prediction results;

[0074] The control module is used to send adjustment data to the execution unit to adjust the angle of the photovoltaic panel.

[0075] The extraction formula of the convolutional neural network feature extraction algorithm is as follows:

[0076] conv(xw)=σ(x * w+bconv)

[0077] Where: conv is the convolution operation; σ is the ReLU function; * is the convolution operation; x is the input data; w and b are the weights and biases of the model, which are learned by training the network.

[0078] The feature calculation prediction model is calculated by the following formula:

[0079] y=β1x1+β2x2+…+βnxn+b

[0080] Where: y is the prediction result, which is the optimal tilt angle of the photovoltaic panel; x1, x2, ..., xn are the extracted eigenvalues; β1, β2, ..., βn and b are the weights and biases of the model, which are learned by training the model.

[0081] After the prediction results are obtained, the best adjustment results are obtained by comparing and analyzing them with the decision recommendations obtained by the analysis unit.

[0082] Specifically, first, the environmental data is input into the convolutional neural network for feature extraction. The convolutional neural network is constructed by the formula conv(xw)=σ(x * w+bconv). x is the input environment data, W and b are the weights and biases of the convolution layer, σ is the ReLU function, and * is the convolution operation. The convolution layer performs convolution operations on the input data x at multiple locations, extracts multiple features, and uses the ReLU function σ to perform nonlinear transformation on the output. The result is a set of new feature values ​​that reflect the characteristics of the input data.

[0083] The AI ​​control unit calculates through the internal prediction model: the features extracted by the convolutional neural network in the previous step are input into the prediction model for calculation. The prediction model uses the formula y = β1x1 + β2x2 + ... + βnxn + b to calculate. x1, x2, ..., xn are the feature values ​​extracted by the convolutional neural network, β1, β2, ..., βn and b are the weights and biases of the prediction model, and y is the output result of the prediction, which is the optimal tilt angle of the photovoltaic panel. The prediction model uses the feature values ​​extracted from the convolutional neural network, merges these features in a weighted manner, and adds the bias term to form the final prediction result y.

[0084] The connection between convolutional neural network (CNN) and prediction model calculation lies in that the output of CNN, i.e. the result of feature extraction, is used as the input of the prediction model for calculation, making the prediction more accurate and robust, and being able to better adjust the tilt angle of photovoltaic panels and improve the utilization rate of solar energy.

[0085] By combining the control unit and the analysis unit, the photovoltaic panel can dynamically adjust the angle of the photovoltaic panel according to the real-time environment and future predictions to maximize the efficiency of solar energy utilization. In addition, it can also predict environmental changes in advance and adjust the angle of the photovoltaic panel in advance to improve system stability and power generation efficiency.

[0086] In addition, the evaluation unit obtains evaluation data through the following formula:

[0087] Eout=η·G·A·cos(θ)·f(C,D,S,M)

[0088] Where: Eout represents the output power of the photovoltaic system; η is the energy conversion efficiency of the photovoltaic panel; G is the intensity of incident solar radiation; A is the effective area of ​​the photovoltaic panel; θ is the angle between the incident light and the normal of the photovoltaic panel;

[0089] f(.) is a comprehensive function; C is the environmental data collected by the environmental sensor; D represents the decision-making recommendations of the analysis unit; S represents the self-learning and optimization of the control unit; M represents the adjustment of the photovoltaic panel position by the actuator;

[0090] The actuator adjusts the position of the photovoltaic panel M according to y generated by the prediction model. The actuator adjusts the position of the photovoltaic panel M according to y generated by the prediction model (i.e., the predicted optimal angle).

[0091] The formula comprehensively considers the physical characteristics of the photovoltaic system itself, as well as the impact of predictive feedback on system performance using an artificial intelligence semantic big model. By maximizing the value of f(C, D, S, M), the best regulation effect can be obtained, improving the overall efficiency of photovoltaic power generation.

[0092] In summary, in the present invention, adaptive control of the photovoltaic support is achieved: the system can calculate the optimal angle of the photovoltaic panel according to environmental data to achieve adaptive control and maximize the efficiency of solar energy utilization.

[0093] Continuous learning and self-optimization are achieved: the analysis unit can use the artificial intelligence semantic big model cloud to analyze the decisions given by the server, and perform self-optimization through continuous learning of environmental data, constantly improving decision-making and control strategies to adapt to changing environmental conditions and improve the adaptability and efficiency of the system.

[0094] It should be further explained that the execution unit can adjust the position of the photovoltaic panel according to the instructions of the control unit. In specific implementation, the execution unit can be composed of multiple motors, electric motors, telescopic rods, etc. to control the angular position of the photovoltaic panel.

[0095] Embodiment 2

[0096] This embodiment proposes a photovoltaic support control method, which is used in an artificial intelligence-based photovoltaic support adaptive control system in the above embodiment 1. Figure 5 As shown, the method includes:

[0097] S1, the environment detection unit collects real-time environment data and transmits it to the analysis unit, control unit and evaluation unit;

[0098] S2, the analysis unit generates decision suggestions based on historical data, real-time data, future predictions, and control unit status;

[0099] S3, the control unit receives the decision of the analysis unit and calculates the optimal angle of the photovoltaic panel based on its own learning optimization results;

[0100] S4, the evaluation unit evaluates the adjusted performance according to the information from the analysis unit and the control unit;

[0101] S5, the control unit sends the final adjustment information to the execution unit, and the execution unit controls the photovoltaic panel to adjust the angle.

[0102] In this embodiment, predictive feedback is realized: by analyzing and predicting environmental data through the analysis unit and the control unit, the system can make accurate decisions, thereby realizing predictive feedback, adjusting the angle of the photovoltaic panels in advance, adapting to future weather changes, and improving the stability of the system and power generation efficiency.

[0103] Embodiment 3

[0104] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method provided in the above-mentioned embodiment 2 are implemented.

[0105] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An artificial intelligence-based photovoltaic support adaptive control system, characterized in that: It includes a control unit, an analysis unit, an environment detection unit, an evaluation unit and an execution unit; The environment detection unit is used to detect the surrounding environment data and feed the data back to the analysis unit, the evaluation unit and the control unit; The analysis unit is used to receive data from the environment detection unit and generate decision suggestions; The control unit is used to receive data from the environment detection unit, extract environmental features and obtain adjustment data according to the decision suggestions of the analysis unit; The execution unit is used to receive data from the control unit and adjust the position angle of the photovoltaic panel; The evaluation unit can evaluate the overall performance based on the adjustment data of the control unit and the decision suggestions of the analysis unit.

2. The photovoltaic support adaptive control system based on artificial intelligence according to claim 1 is characterized in that: The control unit includes a collection module, a control module, and a prediction module; The collection module is used to receive information from the environment detection unit and extract data features according to a convolutional neural network feature extraction algorithm; The prediction module is used to input data features into the feature calculation prediction model to obtain prediction results; The control module is used to send adjustment data to the execution unit to adjust the angle of the photovoltaic panel.

3. The photovoltaic support adaptive control system based on artificial intelligence according to claim 2 is characterized by: The convolutional neural network feature extraction algorithm extraction formula is as follows: conv(xw)=σ(x * w+bconv) Where: conv is the convolution operation; σ is the ReLU function; * is the convolution operation; x is the input data; w and b are the weights and biases of the model, which are learned by training the network.

4. The photovoltaic support adaptive control system based on artificial intelligence according to claim 3 is characterized by: The feature calculation prediction model is calculated by the following formula: y=β1x1+β2x2+…+βnxn+b Where: y is the prediction result, which is the optimal tilt angle of the photovoltaic panel; x1, x2, ..., xn are the extracted eigenvalues; β1, β2, ..., βn and b are the weights and biases of the model, which are learned by training the model.

5. The photovoltaic support adaptive control system based on artificial intelligence according to claim 4 is characterized in that: The analysis unit includes a decision module and a storage module for storing historical data; The analysis unit is used to obtain decision suggestions through the following formula: D=F(H,C,P(C),S) Where: D is the decision structure; H is the historical data environment data; C is the implementation environment data; P(C) is the prediction of the future environment; F(.) is the decision function. The relationship between H, C, P(C) and S is learned through a deep neural network, and the optimal D is predicted based on the current input.

6. The photovoltaic support adaptive control system based on artificial intelligence according to claim 5 is characterized by: The analysis unit also includes an optimization module, which combines a neural network and a support vector machine to obtain a final decision by voting or weighted averaging.

7. The photovoltaic support adaptive control system based on artificial intelligence according to claim 6 is characterized by: The evaluation unit obtains the evaluation data through the following formula: Eout=η·G·A·cos(θ)·f(C,D,S,M) Where: Eout represents the output power of the photovoltaic system; η is the energy conversion efficiency of the photovoltaic panel; G is the intensity of incident solar radiation; A is the effective area of ​​the photovoltaic panel; θ is the angle between the incident light and the normal of the photovoltaic panel; f(.) is the comprehensive function; C is the environmental data collected by the environmental sensor; D represents the decision recommendation of the analysis unit; S stands for the self-learning and optimization of the control unit; M stands for the adjustment of the position of the photovoltaic panel by the actuator; The actuator adjusts the position of the photovoltaic panel M according to y generated by the prediction model.

8. A photovoltaic support control method, characterized in that: The method is applied to an artificial intelligence-based photovoltaic support adaptive control system as described in any one of claims 1 to 7 above: The control method comprises the following steps: S1, the environment detection unit collects real-time environment data and transmits it to the analysis unit, control unit and evaluation unit; S2, the analysis unit generates decision suggestions based on historical data, real-time data, future predictions, and control unit status; S3, the control unit receives the decision of the analysis unit and calculates the optimal angle of the photovoltaic panel based on its own learning optimization results; S4, the evaluation unit evaluates the adjusted performance according to the information from the analysis unit and the control unit; S5, the control unit sends the final adjustment information to the execution unit, and the execution unit controls the photovoltaic panel to adjust the angle.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in claim 8 are implemented.

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